(µ + λ) 具有社会认知突变的进化策略

Q4 Engineering

Journal of Automation, Mobile Robotics and Intelligent Systems Pub Date : 2024-04-04 DOI:10.14313/jamris/1-2024/1

Aleksandra Urbanczyk, Krzysztof Kucaba, Mateusz Wojtulewicz, Marek Kisiel-Dorohinicki, L. Rutkowski, Piotr Duda, Janusz Kacprzyk, Siang Yew Chong, Xin Yao, A. Byrski

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引用次数: 0

摘要

社会认知计算是我们研究小组在过去几年中开发的一种范式。它包括将受个体间学习和认知启发的机制引入元启发式计算。该范式的不同版本已成功应用于蚁群优化（ACO）、粒子群优化（PSO）、遗传算法、差分进化和进化多代理系统（EMAS）元启发式算法的混合。在本文中，我们根据以往的经验，提出了一种基于社会认知机制的新型突变，并在进化策略（ES）的基础上对其进行了测试。我们将新构建的版本应用于流行的基准，并与其参考版本进行比较。

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(µ + λ) Evolution Strategy with Socio-cognitive Mutation

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on sociocognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

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来源期刊

Journal of Automation, Mobile Robotics and Intelligent Systems Engineering-Control and Systems Engineering

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： Fundamentals of automation and robotics Applied automatics Mobile robots control Distributed systems Navigation Mechatronics systems in robotics Sensors and actuators Data transmission Biomechatronics Mobile computing